Optical lithography is an important part of the process flow of Integrated Circuit (IC) manufacturing and involves the transfer of features from a mask onto a silicon wafer. During optical lithography, light is shone onto a mask pattern, which makes an imprint on the resist that lies over the silicon wafer, on the image plane. The proper functioning of the circuit on the mask depends on the accuracy of the transfer of the pattern from the mask to the silicon wafer.
Simulations of optical lithography are utilized to improve the eventual design. For example, optical lithography simulation is used to predict distortions so that they can be corrected during design. Unfortunately, optical lithography simulation, commonly referred to as “litho simulation,” is slow because it is a computationally intensive and demanding task. Despite the demands of optical lithography simulation, completing this process can be a determining factor in time to market for the resultant chips. Thus, finding more efficient ways to complete accurate simulations is desirable because of the potential to improve both the performance and the capacity of these optical simulations.
To decrease the aforementioned heavy computational load required for an optical lithography simulation of a full-chip scale and increase the speed of the simulation, non-physical compact models are sometimes utilized, but this technique has obvious drawbacks. For one, the internal structure of these models is (to a substantial degree) not physically derived, and so does not inherently ensure accurate predictions. Additionally, compact models achieve accuracy through calibration against measurements and obtaining these measurements can be an intensive process that leaves room for errors. In fact, the aforementioned, lack of physical grounding in compact models can make the diagnosis of accuracy imperfections quite difficult. Although modeling and calibration procedures have evolved that provide adequate accuracy from manageable volumes of calibration data, when used by skilled practitioners, significant time and expense is often entailed, and greater accuracy would be preferred. Improvements to existing methodologies and processes, including continued iterations of standard modeling methodologies, have yielded only small incremental improvements in model accuracy. In fact, lengthy efforts to improve the accuracy of these approaches have yielded only modest additional improvements.